1. Data Analytics-introduction

1. Data Analytics-introduction

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  Data Analytics-Introduction K KSingh, RGUKT Nuzvid 19-08-2017KK Singh, RGUKT Nuzvid 1
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  Outcomes Students would learn. 1.Basic definition of Data, Information, and Data analytics 2. Different types of variables 3. Types of analytics 4. Analytics Life Cycle 19-08-2017KK Singh, RGUKT Nuzvid 2
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  Basic Definition  Data:Data is a set of values of qualitative or quantitative variables. It is information in raw or unorganized form. It may be a fact, figure, characters, symbols etc.  Information: Meaningful or organised data is information.  Analytics: Analytics is the discovery , interpretation, and communication of meaningful patterns or summery in data.  Data Analytics (DA) is the process of examining data sets in order to draw conclusion about the information it contains.  Analytics is not a tool or technology, rather it is the way of thinking and acting on data. 19-08-2017KK Singh, RGUKT Nuzvid 3
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  Fathima 50 DA, 70 DA Hema….. 19-08-2017KK Singh, RGUKT Nuzvid 4
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  Data Analytics (Cont..) Examples 1. Business analytics 2. Risk ,, 3. Fraud ,, 4. Health ,, 5. Web ,,  Types of analytics 1. Descriptive Analytics (“What has happened?”) (Data aggregation, summary, data mining) 2. Predictive Analytics (“What might happen?”) (Regression, LSE,MLE) 3. Prescriptive Analytics (“What should we do?”) (Optimization, Recommendation) 19-08-2017KK Singh, RGUKT Nuzvid 5
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  Variable Types Variable Numerical Continuous (ex:height, weight, profit.) Discrete ( # items, population, count of students etc) categorical Nominal categorical ( location, caste, gender ) Ordinal categorical ( Grade, hotness, coldness etc.) 19-08-2017KK Singh, RGUKT Nuzvid 6
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  Answer the question 1. Which type of variable is “Street Number” ?  2. Which type of variable is “Phone Number” ?  3. Which type of variable is “Annual Income” ? 19-08-2017KK Singh, RGUKT Nuzvid 7
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  Analytics Life Cycle 1.Problem Identification 2. Hypothesis formulation 3. Data Collection 4. Data Exploration/preparation 5. Model Building 6. Model Validation and Evaluation 19-08-2017KK Singh, RGUKT Nuzvid 8
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  Analytics Life Cycle(Cont..) 1.Problem Identification  The problem is a situation which is judged to be corrected or solved  Problem can be identified through 1. Comparative/benchmarking studies 2. Performance Reporting 3. Asking some basic questions a) Who are affected by the problem? b) What will happen if problem is not solved? c) When and where does the problem occur? d) Why is the problem occurring e) How are the people currently handling the problem? 19-08-2017KK Singh, RGUKT Nuzvid 9
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  Analytics Life Cycle(Cont..) 2.Hypothesis formulation 1. Frame the questions which need to be answered. 2. Develop a comprehensive list of all possible issues related to the problem. 3. Reduce the list by eliminating duplicates and combining overlapping issues. 4. Using consensus building get down to a major issue list. 3. Data Collection Data collection techniques are 1. Using data that is already collected by others 2. Systematically selecting and watching characteristics of people, objects, and events. 3. Oral questioning respondents either individually or as a group 4. Collecting data based on answers provided by the respondents in written format. 19-08-2017KK Singh, RGUKT Nuzvid 10
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  Analytics Life Cycle(Cont..) 4.Data Exploration 1. Importing data 2. Variable Identification 3. Data Cleaning 4. Summarizing data 5. Selecting subset of data 5. Model Building  Building a Model is a very iterative process because there is no such thing as final and perfect solution.  Many of the machine learning and statistical techniques are available in traditional technology platform 19-08-2017KK Singh, RGUKT Nuzvid 11
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  6. Model validationand Evaluation  Like model building the process of validating model is also a iterative process.  There are so many ways …  Confusion Matrix.  Confidence Interval.  ROC curve  Chi Square.  Root Mean Square Error  Gain and Lift Chart. 19-08-2017KK Singh, RGUKT Nuzvid 12
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  19-08-2017KK Singh, RGUKTNuzvid 13 Any query???
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  Assignment  What isthe probability a student is a female and has no remedial ?  What is the probability that student has no remedial given the student is a boy ?  What is average throughput (internet) in K3 block ?  Plot channel utilization per day in July 2017 in k3 block ? 19-08-2017KK Singh, RGUKT Nuzvid 14